分组汇总统计

Julia 中的数据操作

Katerina Zahradova

Instructor

我们已掌握

# 计算最低工资的平均值
combine(wages, :effective_min_wage_2020_dollars => mean)

1x1 DataFrame
Row | effective_min_wage_2020_dollars_mean
     | Float64
____|_______________________
   1| 8.37093

我们已掌握

# 过滤并计算平均值
first(combine(filter(r -> r.region =="W", wages), :effective_min_wage_2020_dollars => mean))
first(combine(filter(r -> r.region =="S", wages), :effective_min_wage_2020_dollars => mean))
first(combine(filter(r -> r.region =="NE", wages), :effective_min_wage_2020_dollars => mean))

DataFrameRow (1 columns)
Row | effective_min_wage_2020_dollars_mean
    | Float64
____|_______________________
   1| 8.75413
...

使用 combine() 和 groupby()

# 按地区分组
wages_by_region = groupby(wages, :region)

# 计算每组平均值
combine(wages_by_region, :effective_min_wage_2020_dollars => mean)

4x2 DataFrame
Row region    effective_min_wage_2020_dollars_mean
    String    Float64
__________________________
1    S        8.15458
2    W        8.59119
3    NE       8.75413
4    MW       8.1514

组合使用 combine() 和 groupby()

# 合并在一起
combine(groupby(wages, :region), :effective_min_wage_2020_dollars => mean)

4x2 DataFrame
Row region    effective_min_wage_2020_dollars_mean
    String    Float64
__________________________
1    S        8.15458
2    W        8.59119
3    NE       8.75413
4    MW       8.1514

使用 combine() 和 groupby()

# 重命名列
combine(groupby(wages, :region), 
        :effective_min_wage_2020_dollars => mean => :average_min_wage_2020_dollars)

4x2 DataFrame
Row region    average_min_wage_2020_dollars
    String    Float64
__________________________
1    S        8.15458
2    W        8.59119
3    NE       8.75413
4    MW       8.1514

单列使用多个函数

# 对单列应用多个函数
combine(groupby(wages, :region), 
                    :effective_min_wage_2020_dollars .=> [mean, median, maximum])

4x4 DataFrame
Row  region  effective_min_wage_2020_dollars_mean  ...
     String  Float64                               ...
____________________________________________________________________________________
1    S       8.15458                               ...
2    W       8.59119                               ...
3    NE      8.75413                               ...
4    MW      8.1514                                ...

单列使用多个函数

# 对单列应用多个函数
combine(groupby(wages, :region), :effective_min_wage_2020_dollars .=> [mean, median] .=> [:average, :median])

4x2 DataFrame
Row region  average  median
    String  Float64  Float64
____________________________
1    S      8.15458  8.0
2    W      8.59119  8.34
...

# 别忘了点号！
combine(groupby(wages, :region), :effective_min_wage_2020_dollars => [mean, median])

ArgumentError: Unrecognized column selector ...

多列使用一个函数

combine(groupby(wages, :region), [:state_min_wage, :federal_min_wage] .=> mean)

4x2 DataFrame
Row region  state_min_wage_mean  federal_min_wage_mean
    String  Float64              Float64
______________________________________________________
1   S       2.73128              4.35566
2    W       4.26638              4.35566
...

# 别忘了点号
combine(groupby(wages, :region), [:state_min_wage, :federal_min_wage] => mean)

MethodError: objects of type ...

多列使用多个函数

# 函数作为1行矩阵
combine(groupby(wages, :region), [:state_min, :federal_min] .=> [mean median])

Row region  state_min_mean  federal_min_mean  state_min_median  federal_min_median
_________________________________________________________________________________
1   S       2.73128         4.35566           2.0                4.25
...

# 函数作为向量
combine(groupby(wages, :region), [:state_min, :federal_min] .=> [mean, median])

Row region  state_min_mean  federal_min_median 
______________________________________________
1   S       2.73128         4.25
...

可用函数

可用的函数：

常见统计函数，如 sum(), mean(), minimum() 等
用户自定义函数（广播）
匿名函数，包在 ByRow() 中
DataFrames 专用函数：nrow、proprow 等

速查表

# 分组后的 DataFrame gdf
# 1 列 + 1 个函数
combine(gdf, :c => f => :new_c)

# 1 列 + 2 个及以上函数
combine(gdf, :c .=> [f1, f2, ...] .=> [:new_c_f1, :new_c_f2, ...])

# 2 列及以上 + 1 个函数
combine(gdf, [:c1, :c2, ...] .=> f .=> [:new_c1_f, :new_c2_f, ...])

# 2 列及以上 + 2 个及以上函数 - 笛卡尔组合
combine(gdf, [:c1, :c2, ...] .=> [f1 f2 ...] .=> [:c1_f1, :c2_f1, ..., :c1_f2, ...])

# 2 列及以上 + 2 个及以上函数 - 成对对应
combine(gdf, [:c1, :c2, ...] .=> [f1, f2, ...] .=> [:new_c1_f1, :new_c2_f2, ...])

Vamos praticar!

Julia 中的数据操作